Survey data on disability among the elderly are available from several sources, most prominently the National Long Term Care Survey (NLTCS). The NLTCS began in 1982 and now extends over five waves through 1999, making it a rich source of information on possible changes in disability over time. But these data pose challenges for both statistical modeling and the protection of confidentiality of the information provided by survey respondents, especially when the data for individuals are linked across waves. Most statistical approaches used to analyze NLTCS data are based on disability scales that cannot account for the complexity of disability manifestations. Attempts to deal with such complexity include traditional multivariate methods for both discrete and continuous data, and approaches based on the grade of membership model. These methods typically require either making heroic simplifying assumptions or need to be adapted. This project aims to develop new statistical models and approaches for the analysis of such survey data, including the role of sample weights in the use of these models. It also proposes to take a fresh look at the risk of inadvertent disclosure of information on NLTCS respondents and to develop new approaches to protect against disclosure while preserving access to the maximal amount of information in the data required for their proper analysis using the new models and methods.